Preliminary report: neural firing patterns specific for Meniere's disease

Evaluating the Diagnostic Value of Electrovestibulography (EVestG) in Alzheimer's Patients with Mixed Pathology: A Pilot Study

Background and Objectives: Diagnosis of dementia subtypes caused by different brain pathophysiologies, particularly Alzheimer's disease (AD) from AD mixed with levels of cerebrovascular disease (CVD) symptomology (AD-CVD), is challenging due to overlapping symptoms. In this pilot study, the potential of Electrovestibulography (EVestG) for identifying AD, AD-CVD, and healthy control populations was investigated. Materials and Methods: A novel hierarchical multiclass diagnostic algorithm based on the outcomes of its lower levels of binary classifications was developed using data of 16 patients with AD, 13 with AD-CVD, and 24 healthy age-matched controls, and then evaluated on a blind testing dataset made up of a new population of 12 patients diagnosed with AD, 9 with AD-CVD, and 8 healthy controls. Multivariate analysis was run to test the between population differences while controlling for sex and age covariates. Results: The accuracies of the multiclass diagnostic algorithm were found to be 85.7% and 79.6% for the training and blind testing datasets, respectively. While a statistically significant difference was found between the populations after accounting for sex and age, no significant effect was found for sex or age covariates. The best characteristic EVestG features were extracted from the upright sitting and supine up/down stimulus responses. Conclusions: Two EVestG movements (stimuli) and their most informative features that are best selective of the above-populations' separations were identified, and a hierarchy diagnostic algorithm was developed for three-way classification. Given that the two stimuli predominantly stimulate the otholithic organs, physiological and experimental evidence supportive of the results are presented. Disruptions of inhibition associated with GABAergic activity might be responsible for the changes in the EVestG features.

Verification EVestG recordings are vestibuloacoustic signals

Introduction

Neural dysfunction is associated with aberrant nerve firing; thus, electrodiagnosis has the potential for objective diagnosis and quantification of neural dysfunction. Electrical stimulation alters nerve firing and may also have treatment potential. This article outlines some findings related to electrodiagnosis and electrical stimulation of the ear. The quasi‐synchronous firing of many vestibuloacoustic nerve fibers can produce an extracellular potential defined as a field potential (FP). Electrovestibulography (EVestG) is a method to record vestibuloacoustic signals and detect the associated FPs. A clear picture of the muscle‐, EEG‐, saccade‐related, or other artefactual origins, and the physiologic basis of FPs recorded with EVestG, is evolving. EVestG was applied to demonstrate the effect of electrical stimulation on spontaneous FPs in the ear canal.

Methods

Bilateral EVestG recordings were conducted on 14 guinea pigs before and after stimulation with 3–0.5 mA ipsilateral anodal electrical pulses before and after ablation via unilateral Scarpa's ganglionectomy to elucidate the origin of the EVestG recorded spontaneous FPs.

Results

Anodal electrical stimulation suppresses the recorded activity. There was a significant reduction of the level of recorded signal observed following anodal stimulation on the ablated but not the intact side.

Conclusion

Electrical stimulation of the external auditory canal reduces spontaneous electrical activity in the ear canal, some of which is due to central nervous system activity. The EVestG recorded FPs have a major vestibuloacoustic component.

An unbiased algorithm for objective separation of Alzheimer's, Alzheimer's mixed with cerebrovascular symptomology, and healthy controls from one another using electrovestibulography (EVestG)

Diagnosis of Alzheimer's disease (AD) from AD with cerebrovascular disease pathology (AD-CVD) is a rising challenge. Using electrovestibulography (EVestG) measured signals, we develop an automated feature extraction and selection algorithm for an unbiased identification of AD and AD-CVD from healthy controls as well as their separation from each other. EVestG signals of 24 healthy controls, 16 individuals with AD, and 13 with AD-CVD were analyzed within two separate groupings: One-versus-One and One-versus-All. A multistage feature selection process was conducted over the training dataset using linear support vector machine (SVM) classification with 10-fold cross-validation, k nearest neighbors/averaging imputation, and exhaustive search. The most frequently selected features that achieved highest classification performance were selected. 10-fold cross-validation was applied via a linear SVM classification on the entire dataset. Multivariate analysis was run to test the between population differences while controlling for the covariates. Classification accuracies of ≥ 80% and 78% were achieved for the One-versus-All classification approach and AD versus AD-CVD separation, respectively. The results also held true after controlling for the effect of covariates. AD/AD-CVD participants showed smaller/larger EVestG averaged field potential signals compared to healthy controls and AD-CVD/AD participants. These characteristics are in line with our previous study results.

Differences Between Physical vs. Virtual Evoked Vestibular Responses

Electrovestibulography (EVestG), a technology purported to measure vestibular activity at the vestibular periphery, was used to compare the vestibular responses to two sensory inputs: (1) back-forward physical tilt (with eyes-open and eyes-closed) and (2) virtual reality replica of the back-forward tilt (eyes-open, physically static). Twenty-seven healthy participants (10 females) were tested. From each of the EVestG recordings, two feature curves: (1) average field potential (FP), and (2) distribution of time intervals between the detected FPs were extracted. For the eyes-closed physical tilt, except for the background segment, the FP response curve was generally wider compared to that evoked during the virtual replica tilt (p < 0.05). Moreover, the eyes-closed physical tilt produced longer time intervals between FP's compared to the virtual stimulus. For this measure, for the background segment, the eyes closed and open physical tilt responses were significantly different (p < 0.05) in both ears (repeated measure experimental design). The results support: (1) both vestibular and visual inputs evoking a measurably different EVestG response, (2) the differences between physical and virtual vestibular responses are dependent on the eyes being either open or closed, and (3) for the stimuli used, the modulation of vestibular afferent activity was measurably smaller for virtual than physical stimulation.

Quantitative separation of the depressive phase of bipolar disorder and major depressive disorder using electrovestibulography

Objectives: No electrophysiological, neuroimaging or genetic markers have been established that strongly relate to the diagnostic separation of bipolar disorder (BD) and major depressive disorder (MDD). This paper's objective is to describe the potential of features, extracted from the recording of electrical activity from the outer ear canal, in a process called electrovestibulography (EVestG), for identifying depressed and partly remitted/remitted MDD and BD patients from each other.Methods: From EVestG data four sensory vestibulo-acoustic features were extracted from both background (no movement) and using a single supine-vertical translation stimulus to distinguish 27 controls, 39 MDD and 43 BD patients.Results: Using leave-one-out-cross-validation, unbiased parametric and non-parametric classification routines resulted in 78-83% (2-3 features), 80-81% (1-2 features) and 66-68% (3 features) accuracies for separation of MDD from BD, controls from depressed (BD & MDD) and the 3-way separation of BD from MDD from control groups, respectively. The main limitations of this study were the inability to fully disentangle the impact of prescribed medication from the responses and also the limited sample size.Conclusions: EVestG features can reliably identify depressed and partly remitted/remitted MDD and BD patients from each other.

Application of Vestibular Spontaneous Response as a Diagnostic Aid for Meniere's Disease

In this paper, we report on a new method for assisting in Meniere's disease diagnosis. An accurate diagnosis of Meniere's is challenging, and requires an expert opinion after observing several clinical assessments and tests over a period of time. Our proposed method is based on the analysis of the spontaneous and driven ear evoked responses recorded using Electrovestibulography (EVestG). We used the EVestG signals of 35 individuals suspected of Meniere's and 26 age-matched healthy controls, out of which data of 14 patients with Meniere's and 16 healthy controls were used for developing the diagnostic algorithm (training set) and the rest for testing. While recording and analyzing the test dataset, the researchers were only aware the patients suffered some dizziness, and were kept blind to the exact diagnoses till the end of study. EVestG field potentials (FPs) and their firing pattern, in response to several whole body tilt stimuli from both left and right ears were extracted. We investigated several features of the extracted FPs in response to each of side, back/forward, rotation, up/down, supine rotation, and supine up/down tilt stimulations, and selected the top five features showing the most significant differences between of the groups of the training set for every tilt. An ad-hoc average voting classifier was designed based on building five single-feature classifiers (using Linear Discriminant analysis) and taking the average of the single-feature classifiers' votes. The results showed the side tilt data were best for the purpose of Meniere's diagnosis; it resulted in 78% and 90% sensitivity and specificity for test dataset, respectively. The second best accuracy was achieved using back/forward tilt. The results and their implications are discussed. Overall, the EVestG side tilt results encourage the use of vestibular response as a non-invasive, robust and quick screening for Meniere's and separating it from other types of dizziness.